Tootfinder

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
Tobias Grafke
https://arxiv.org/abs/2506.15743 https://arxiv.org/…

Sampling conditioned diffusions via Pathspace Projected Monte Carlo
We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic integral constraints. The algorithm is a pathspace Metropolis-adjusted manifold sampling scheme, which samples stochastic paths on the submanifold of realizations that adhere to the conditioning constraint. We demonstrate the effectiveness of the algorithm by sampling a dynamical condensation phase transition, conditi…

Constrained Control Allocation With Continuous-Time Rate Constraints: Three-Dimensional Case
S\"uleyman \"Ozkurt, Adrian Grimm, Walter Fichter
https://arxiv.org/abs/2507.15489

Constrained Control Allocation With Continuous-Time Rate Constraints: Three-Dimensional Case
This paper presents a novel quadratic programming (QP) approach for constrained control allocation that directly incorporates continuous-time actuator rate constraints without requiring slack variables. Over-actuated aircraft configurations, particularly prevalent in eVTOL and military applications, require control allocation algorithms to distribute commanded control moments among available actuators while respecting position and rate constraints. Existing methods such as direct allocation, ps…

Low-Energy Supernova Constraints on Lepton Flavor Violating Axions
Zi-Miao Huang, Zuowei Liu
https://arxiv.org/abs/2506.16922 https://

Low-Energy Supernova Constraints on Lepton Flavor Violating Axions
The extreme conditions within the supernova core, a high-temperature and high-density environment, create an ideal laboratory for the search for new physics beyond the Standard Model. Of particular interest are low-energy supernovae, characterized by their low explosion energies, which place strong constraints on the new-physics energy transfer from the core to the mantle. We compute low-energy supernova constraints on lepton-flavor-violating axions and axion-like particles that couple to both …

Differential constraints for hyperbolic systems through k-Riemann invariants
Alessandra Jannelli, Natale Manganaro, Alessandra Rizzo
https://arxiv.org/abs/2507.14877

Differential constraints for hyperbolic systems through k-Riemann invariants
In this paper we develop a reduction procedure for determining exact wave solutions of first order quasilinear hyperbolic one-dimensional nonhomogeneous systems. The approach is formulated within the theoretical framework of the method of differential constraints and it makes use of the $k-$Riemann invariants. The solutions obtained permit to characterize rarefaction waves also for nonhomogeneous models so that Riemann problems can be solved. Applications to the Euler system describing an ideal…

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Zhenyun Yin, Shujie Wang, Xuhong Wang, Xingjun Ma, Yinchun Wang
https://arxiv.org/abs/2507.16727

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the first framework to integrate certainty calibration with retrieval-based search for open-domain question answering. The agent performs multi-step reflection and verification over Wikipedia data and is trained with a reinforcement learning algorithm that optimizes for accuracy under a soft reliability constraint. Empirical …

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR
Jiakang Wang, Runze Liu, Fuzheng Zhang, Xiu Li, Guorui Zhou
https://arxiv.org/abs/2507.15778

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR
Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large Language Models (LLMs), mainly by shaping higher-order behaviors such as reflection and planning. However, previous RLVR algorithms often apply uniform training signals to all tokens, without considering the different roles of low-entropy knowledge-related tokens and high-entropy reasoning-related tokens. Some recent methods try to separate these toke…

Hyperparameter-free minimum-lengthscale constraints for topology optimization
Rodrigo Arrieta, Giuseppe Romano, Steven G. Johnson
https://arxiv.org/abs/2507.16108

Hyperparameter-free minimum-lengthscale constraints for topology optimization
The geometric constraints of Zhou et al. (2015) are a widely used technique in topology/freeform optimization to impose minimum lengthscales for manufacturability. However, its efficacy degrades as design binarization is increased, and it requires heuristic tuning of multiple hyperparameters. In this work, we derive analytical hyperparameters from first principles, depending only on the target lengthscale. We present results for both conic and PDE-based filtering schemes, showing that the latte…

Rate-Distortion-Perception Trade-off with Strong Realism Constraints: Role of Side Information and Common Randomness
Yassine Hamdi, Aaron B. Wagner, Deniz G\"und\"uz
https://arxiv.org/abs/2507.14825

Rate-Distortion-Perception Trade-off with Strong Realism Constraints: Role of Side Information and Common Randomness
In image compression, with recent advances in generative modeling, existence of a trade-off between the rate and perceptual quality has been brought to light, where the perceptual quality is measured by the closeness of the output and source distributions. We consider the compression of a memoryless source sequence $X^n=(X_1, \ldots, X_n)$ in the presence of memoryless side information $Z^n=(Z_1, \ldots, Z_n),$ originally studied by Wyner and Ziv, but elucidate the impact of a strong perfect re…

Constraints on the Sen black hole mass and charge from quasi-periodic oscillations
Kuantay Boshkayev, Marco Muccino
https://arxiv.org/abs/2508.15682 https://

Constraints on the Sen black hole mass and charge from quasi-periodic oscillations
We analyze quasi-periodic oscillation data from selected X-ray binary systems hosting black holes. To model the spacetime geometry, we resort the static Sen solution -- originally derived in the framework of heterotic string theory -- which reduces to the Schwarzschild spacetime for vanishing electric charge. By fitting the observed frequencies within the relativistic precession model, we constrain the mass and charge parameters of the Sen black hole and discuss their astrophysical implications…

10,000 Resolved Triples from Gaia: Empirical Constraints on Triple Star Populations
Cheyanne Shariat, Kareem El-Badry, Smadar Naoz
https://arxiv.org/abs/2506.16513

10,000 Resolved Triples from Gaia: Empirical Constraints on Triple Star Populations
We present a catalog of $\sim 10,000$ resolved triple star systems within 500 pc of the Sun, constructed using Gaia data. The triples include main-sequence, red giant, and white dwarf components spanning separations of 10 to 50,000 au. A well-characterized selection function allows us to constrain intrinsic demographics of the triple star population. We find that (a) all systems are compatible with being hierarchical and dynamically stable; (b) mutual orbital inclinations are isotropic for wide…

Planck constraints on the scale dependence of isotropic cosmic birefringence
M. Ballardini, A. Gruppuso, S. Paradiso, S. S. Sirletti, P. Natoli
https://arxiv.org/abs/2507.16714

Planck constraints on the scale dependence of isotropic cosmic birefringence
The rotation of the linear polarisation plane of photons during propagation, also known as cosmic birefringence, is a powerful probe of parity-violating extensions of standard electromagnetism. Using Planck legacy data, we confirm previous estimates of the isotropic birefringence angle, finding $β\simeq 0.30 \pm 0.05$ [deg] at 68% CL, not including the systematic error from the instrumental polarisation angle. If this is a genuine signal, it could be explained by theories of Chern--Simons-type…

Safety Controller Synthesis for Stochastic Networked Systems under Communication Constraints
Omid Akbarzadeh, Mohammad H. Mamduhi, Abolfazl Lavaei
https://arxiv.org/abs/2507.15031

Safety Controller Synthesis for Stochastic Networked Systems under Communication Constraints
This paper develops a framework for synthesizing safety controllers for discrete-time stochastic linear control systems (dt-SLS) operating under communication imperfections. The control unit is remote and communicates with the sensor and actuator through an imperfect wireless network. We consider a constant delay in the sensor-to-controller channel (uplink), and data loss in both sensor-to-controller and controller-to-actuator (downlink) channels. In our proposed scheme, data loss in each chann…

DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints
Xuezheng Qin, Ruwei Huang, Xiaolong Tang, Feng Li
https://arxiv.org/abs/2508.14530

DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints
Federated Learning (FL) is increasingly adopted for privacy-preserving collaborative training, but its decentralized nature makes it particularly susceptible to backdoor attacks. Existing attack methods, however, often rely on idealized assumptions and fail to remain effective under real-world constraints, such as limited attacker control, non-IID data distributions, and the presence of diverse defense mechanisms. To address this gap, we propose DOPA (Divergent Optimization Path Attack), a nove…

Close is Good Enough: Component-Based Synthesis Modulo Logical Similarity
Ashish Mishra, Suresh Jagannathan
https://arxiv.org/abs/2508.14614 https://arxiv.…

Close is Good Enough: Component-Based Synthesis Modulo Logical Similarity
Component-based synthesis (CBS) aims to generate loop-free programs from a set of libraries whose methods are annotated with specifications and whose output must satisfy a set of logical constraints, expressed as a query. The effectiveness of a CBS algorithm critically depends on the severity of the constraints imposed by the query. The more exact these constraints are, the sparser the space of feasible solutions. This maxim also applies when we enrich the expressiveness of the specifications a…

X-ray and Radio Analysis of Abell 1644: Constraints on Cluster Dynamics
Humaira Bashir, R. Kale, Asif Iqbal, Manzoor A. Malik
https://arxiv.org/abs/2506.16197

X-ray and Radio Analysis of Abell 1644: Constraints on Cluster Dynamics
We present the first band-2 (120 - 250 MHz) uGMRT (upgraded Giant Metrewave Radio Telescope) observations of the bimodal galaxy cluster Abell 1644 (z = 0.0471), complemented by Chandra X-ray data. While weak lensing measurements reveal a third substructure in Abell 1644, our radio analysis reveals only two compact sources coinciding with the respective brightest cluster galaxies (BCGs) of the northern (A1644N) and southern (A1644S) substructures, seen in the X-ray observations. Radio analysis y…

Geometric design of the tangent term in landing algorithms for orthogonality constraints
Florentin Goyens, P. -A. Absil, Florian Feppon
https://arxiv.org/abs/2507.15638

Geometric design of the tangent term in landing algorithms for orthogonality constraints
We propose a family a metrics over the set of full-rank $n\times p$ real matrices, and apply them to the landing framework for optimization under orthogonality constraints. The family of metrics we propose is a natural extension of the $β$-metric, defined on the Stiefel manifold.

Thermodynamics of dilute anyon gases from fusion constraints
Yuto Nakajima, Umang Mehta, Hart Goldman
https://arxiv.org/abs/2508.14961 https://arxiv.org/pd…

Thermodynamics of dilute anyon gases from fusion constraints
Recent measurements on 2d materials tuning between fractional quantum anomalous Hall phases and a plethora of correlated electronic states call for a detailed understanding of the dynamics of anyons. Here we develop a general theory of the statistical mechanics of anyon gases at finite temperature, valid in regimes where the anyons are sufficiently dilute and can be treated as weakly interacting particles. We find that with a minimal set of universal braiding and fusion data, along with informa…

#ScribesAndMakers 22
Show us something you've created. Tell us the story behind it.
https://cs.wellesley.edu/~pmwh/labyrinfinite/
I was thinking on a dog walk about how most games with procedurally generated content like Minecraft get pretty repetitive at some point if you zoom out far enough, and large-scale structures that have structural constraints like rivers are very hard to generate piecemeal. So I wanted to come up with an algorithm that could generate globally-consistent structures piece-by-piece, with consistency even if pieces were generated out-of-order, while maintaining only a fixed amount of context no matter how far from the origin you went. This demo is *almost* that, except the amount of context scales logarithmically with the distance-from-origin, which I find a very acceptable compromise. In the demo, there's a single infinitely-long path that eventually touches every cell of the infinite 2D grid (okay, computer limitations mean it's not really infinite, but mathematically it could be). You can get different path structures from different random seeds, although the generation trick does constrain things a lot relative to the set of all possible such paths (notice that in each 5x5 region it touches every cell before leaving; that's not in general necessary).

Germany faces constraints in supplying more patriot air-defense systems to Ukraine, says Defense Minister: https://benborges.xyz/2025/07/14/germany-faces-constraints-in-supplying.html

Impact of $\Xi$-Hypernuclear Constraints on Relativistic Equation of States and Properties of Hyperon Stars
Shi-Yuan Ding, Xiang-Dong Sun, Bao-Yuan Sun, Ang Li
https://arxiv.org/abs/2506.16871

Impact of $Ξ$-Hypernuclear Constraints on Relativistic Equation of States and Properties of Hyperon Stars
Significant uncertainties persist in describing the equation of state and internal structure of hyperon stars due to the limited understanding of the mechanisms underlying hyperon interactions. Constraining the interaction parameter space through a combination of the latest astronomical observations and hypernuclear physics experiments is therefore essential. In this study, we incorporate experimental constraints from $Ξ$ hypernuclear physics on top of $Λ$ hyperons considered in \citet{Sun202…

Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints
Matthew P. Adams, Gloria M. Monsalve-Bravo, Lucy G. Dowdell, Scott A. Sisson, Christopher Drovandi
https://arxiv.org/abs/2508.15292

Efficient sampling from a multivariate normal distribution subject to linear equality and inequality constraints
Sampling from multivariate normal distributions, subjected to a variety of restrictions, is a problem that is recurrent in statistics and computing. In the present work, we demonstrate a general framework to efficiently sample a multivariate normal distribution subject to any set of linear inequality constraints and/or linear equality constraints simultaneously. In the approach we detail, sampling a multivariate random variable from the domain formed by the intersection of linear constraints pr…

Eco-Innovation and Earnings Management: Unveiling the Moderating Effects of Financial Constraints and Opacity in FTSE All-Share Firms
Probowo Erawan Sastroredjo, Marcel Ausloos, Polina Khrennikova
https://arxiv.org/abs/2508.14935

Eco-Innovation and Earnings Management: Unveiling the Moderating Effects of Financial Constraints and Opacity in FTSE All-Share Firms
Our research investigates the relationship between eco-innovation and earnings management among 567 firms listed on the FTSE All-Share Index from 2014 to 2022. By examining how sustainability-driven innovation influences financial reporting practices, we explore the strategic motivations behind income smoothing in firms engaged in environmental initiatives. The findings reveal a positive association between eco-innovation and earnings management, suggesting that firms may leverage ecoinnovation…

Cross-Modality Controlled Molecule Generation with Diffusion Language Model
Yunzhe Zhang, Yifei Wang, Khanh Vinh Nguyen, Pengyu Hong
https://arxiv.org/abs/2508.14748 https://

Cross-Modality Controlled Molecule Generation with Diffusion Language Model
Current SMILES-based diffusion models for molecule generation typically support only unimodal constraint. They inject conditioning signals at the start of the training process and require retraining a new model from scratch whenever the constraint changes. However, real-world applications often involve multiple constraints across different modalities, and additional constraints may emerge over the course of a study. This raises a challenge: how to extend a pre-trained diffusion model not only t…

Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
Keita Kobashi, Masayoshi Tomizuka
https://arxiv.org/abs/2507.14721 https:/…

Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
This study addresses the problem of occluded grasping, where primary grasp configurations of an object are not available due to occlusion with environment. Simple parallel grippers often struggle with such tasks due to limited dexterity and actuation constraints. Prior works have explored object pose reorientation such as pivoting by utilizing extrinsic contacts between an object and an environment feature like a wall, to make the object graspable. However, such works often assume the presence …

On Entropy Bounds for Irrelevant Operators
Lucas Fern\'andez-Sarmiento, Riccardo Penco, Rachel A Rosen
https://arxiv.org/abs/2508.14978 https://arxiv.o…

On Entropy Bounds for Irrelevant Operators
Consistency constraints for low-energy theories, especially those lacking Lorentz invariance, have recently garnered attention. Building on results from black hole thermodynamics, we investigate the conjecture that leading irrelevant deformations of a conformal field theory in the infrared must increase the system's entropy. We show that this entropy-positivity conjecture is equivalent to a decrease in the thermal grand potential at a fixed temperature. We then evaluate this proposal against va…

OSTRICH2: Solver for Complex String Constraints
Matthew Hague, Denghang Hu, Artur Je\.z, Anthony W. Lin, Oliver Markgraf, Philipp R\"ummer, Zhilin Wu
https://arxiv.org/abs/2506.14363

OSTRICH2: Solver for Complex String Constraints
We present OSTRICH2, the latest evolution of the SMT solver OSTRICH for string constraints. OSTRICH2 supports a wide range of complex functions on strings and provides completeness guarantees for a substantial fragment of string constraints, including the straight-line fragment and the chain-free fragment. OSTRICH2 provides full support for the SMT-LIB theory of Unicode strings, extending the standard with several unique features not found in other solvers: among others, parsing of ECMAScript r…

Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
Zhou Li, Xiang Zhang, Jiawen Lv, Jihao Fan, Haiqiang Chen, Giuseppe Caire
https://arxiv.org/abs/2507.14768

Collusion-Resilient Hierarchical Secure Aggregation with Heterogeneous Security Constraints
Motivated by federated learning (FL), secure aggregation (SA) aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation (HSA) investigated the communication and secret key generation efficiency in a 3-layer relay network, where clusters of users are connected to the aggregation server through an intermediate layer of relays. Due to the pre-aggregation of the messag…

SimuPanel: A Novel Immersive Multi-Agent System to Simulate Interactive Expert Panel Discussion
Xiangyang He, Jiale Li, Jiahao Chen, Yang Yang, Mingming Fan
https://arxiv.org/abs/2506.16010

SimuPanel: A Novel Immersive Multi-Agent System to Simulate Interactive Expert Panel Discussion
Panel discussion allows the audience to learn different perspectives through interactive discussions among experts moderated by a host and a Q&A session with the audience. Despite its benefits, panel discussion in the real world is inaccessible to many who do not have the privilege to participate due to geographical, financial, and time constraints. We present SimuPanel, which simulates panel discussions among academic experts through LLM-based multi-agent interaction. It enables users to defin…

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Yupeng Jiang, Shuaiyi Sun, Xi Zheng
https://arxiv.org/abs/2506.16101

Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques
Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. A…

Hierarchical constraints on gravitational waves from horizonless compact objects
Rajrupa Mondal, Julian Westerweck, Yotam Sherf, Collin D. Capano
https://arxiv.org/abs/2506.17215 …

Hierarchical constraints on gravitational waves from horizonless compact objects
We use the data of several promising gravitational wave observations to obtain increasingly stringent bounds on near-horizon deviations of their sources from the Kerr geometry. A range of horizonless compact objects proposed as alternatives to black holes of general relativity would possess a modified gravitational wave emission after the merger. Modelling these objects by introducing reflection of gravitational waves near the horizon, we can measure deviations from Kerr in terms of a single ad…

The electroweak precision constraints of the 2HDM S
Cheng Li, Juxiang Li, Shufang Su, Wei Su
https://arxiv.org/abs/2507.14288 https://

The electroweak precision constraints of the 2HDM+S
The 2HDM+S is the singlet extension of the Two-Higgs-Doublets Model (2HDM). The singlet field and its mixing with the 2HDM Higgs sector lead to new contributions to the electroweak precision observables, in particular, the oblique parameters. In this paper, we identify five benchmark cases, where at most one mixing angle is nonzero and analyze the 95% C.L. allowed parameter space by the oblique parameters. In the alignment limit of the 2HDM, we find that other than the usual mass relations of $…

Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices
Haitian Wang, Xinyu Wang, Yiren Wang, Karen Lee, Zichen Geng, Xian Zhang, Kehkashan Kiran, Yu Zhang, Bo Miao
https://arxiv.org/abs/2507.15958

Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices
Accurate and efficient skin lesion classification on edge devices is critical for accessible dermatological care but remains challenging due to computational, energy, and privacy constraints. We introduce QANA, a novel quantization-aware neuromorphic architecture for incremental skin lesion classification on resource-limited hardware. QANA effectively integrates ghost modules, efficient channel attention, and squeeze-and-excitation blocks for robust feature representation with low-latency and e…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Michael Sun, Tai Vu, Andrew Wang
https://arxiv.org/abs/2508.14905 https://arxi…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item in…

Characterizing Communication Patterns in Distributed Large Language Model Inference
Lang Xu, Kaushik Kandadi Suresh, Quentin Anthony, Nawras Alnaasan, Dhabaleswar K. Panda
https://arxiv.org/abs/2507.14392

Characterizing Communication Patterns in Distributed Large Language Model Inference
Large Language Models (LLMs) built on transformer architectures have transformed natural language processing, achieving remarkable performance across diverse applications. While distributed inference frameworks enable practical deployment of these models, inter-GPU communication creates significant performance constraints that limit service quality in real-world systems. This paper investigates communication dynamics in distributed LLM serving-analyzing how various parallelization approaches co…

Nonlinear Framework for Speech Bandwidth Extension
Tarikul Islam Tamiti, Nursad Mamun, Anomadarshi Barua
https://arxiv.org/abs/2507.15970 https://

Nonlinear Framework for Speech Bandwidth Extension
Recovering high-frequency components lost to bandwidth constraints is crucial for applications ranging from telecommunications to high-fidelity audio on limited resources. We introduce NDSI-BWE, a new adversarial Band Width Extension (BWE) framework that leverage four new discriminators inspired by nonlinear dynamical system to capture diverse temporal behaviors: a Multi-Resolution Lyapunov Discriminator (MRLD) for determining sensitivity to initial conditions by capturing deterministic chaos, …

Forecast constraints on null tests of the $\Lambda$CDM model with SPHEREx
Alejandro Mata Rom\'an, Indira Ocampo, Savvas Nesseris
https://arxiv.org/abs/2507.16604

Forecast constraints on null tests of the $Λ$CDM model with SPHEREx
In this work we quantify the ability of the upcoming SPHEREx survey to constrain cosmological observables and test the internal consistency of the cosmological constant and cold dark matter ($Λ$CDM) model. Using Fisher matrix forecasting, we assess the expected precision on Baryon Acoustic Oscillations (BAO) observables, such as the angular diameter distance $D_\mathrm{A}(z)$ and the Hubble parameter $H(z)$. We further explore SPHEREx's potential to probe some of the fundamental assumptions of…

A Statistician's Overview of Mechanistic-Informed Modeling
Christopher K. Wikle, Joshua North, Giri Gopalan, Myungsoo Yoo
https://arxiv.org/abs/2507.14336

A Statistician's Overview of Mechanistic-Informed Modeling
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that …

1.64-Approximation for Chromatic Correlation Clustering via Chromatic Cluster LP
Dahoon Lee, Chenglin Fan, Euiwoong Lee
https://arxiv.org/abs/2507.15417 ht…

1.64-Approximation for Chromatic Correlation Clustering via Chromatic Cluster LP
Chromatic Correlation Clustering (CCC) generalizes Correlation Clustering by assigning multiple categorical relationships (colors) to edges and imposing chromatic constraints on the clusters. Unlike traditional Correlation Clustering, which only deals with binary $(+/-)$ relationships, CCC captures richer relational structures. Despite its importance, improving the approximation for CCC has been difficult due to the limitations of standard LP relaxations. We present a randomized $1.64$-approxim…

Delta: A Learned Mixed Cost-based Query Optimization Framework
Jiazhen Peng, Zheng Qu, Xiaoye Miao, Rong Zhu
https://arxiv.org/abs/2506.15848 https://

Delta: A Learned Mixed Cost-based Query Optimization Framework
Query optimizer is a crucial module for database management systems. Existing optimizers exhibit two flawed paradigms: (1) cost-based optimizers use dynamic programming with cost models but face search space explosion and heuristic pruning constraints; (2) value-based ones train value networks to enable efficient beam search, but incur higher training costs and lower accuracy. They also lack mechanisms to detect queries where they may perform poorly. To determine more efficient plans, we propos…

MASC: Integrated Sensing and Communications for the Martian Internet of Space
Haofan Dong, Ozgur B. Akan
https://arxiv.org/abs/2506.16198 https://

MASC: Integrated Sensing and Communications for the Martian Internet of Space
Mars exploration missions increasingly demand reliable communication systems, yet harsh environmental conditions -- particularly frequent dust storms, extreme Doppler effects, and stringent resource constraints -- pose unprecedented challenges to conventional communication approaches. This paper presents the Martian Adaptive Sensing and Communication (MASC) system specifically designed for the Martian environment. MASC establishes a physically interpretable channel model and develops three key …

RCNet: $\Delta\Sigma$ IADCs as Recurrent AutoEncoders
Arnaud Verdant, William Guicquero, J\'er\^ome Chossat
https://arxiv.org/abs/2506.16903 https://…

RCNet: $ΔΣ$ IADCs as Recurrent AutoEncoders
This paper proposes a deep learning model (RCNet) for Delta-Sigma ($ΔΣ$) ADCs. Recurrent Neural Networks (RNNs) allow to describe both modulators and filters. This analogy is applied to Incremental ADCs (IADC). High-end optimizers combined with full-custom losses are used to define additional hardware design constraints: quantized weights, signal saturation, temporal noise injection, devices area. Focusing on DC conversion, our early results demonstrate that $SNR$ defined as an Effective Numb…

Multi-objective Portfolio Optimization Via Gradient Descent
Christian Oliva, Pedro R. Ventura, Luis F. Lago-Fern\'andez
https://arxiv.org/abs/2507.16717 https://

Multi-objective Portfolio Optimization Via Gradient Descent
Traditional approaches to portfolio optimization, often rooted in Modern Portfolio Theory and solved via quadratic programming or evolutionary algorithms, struggle with scalability or flexibility, especially in scenarios involving complex constraints, large datasets and/or multiple conflicting objectives. To address these challenges, we introduce a benchmark framework for multi-objective portfolio optimization (MPO) using gradient descent with automatic differentiation. Our method supports any …

Measurements of the z=4-10 X-ray Luminosity Function: the high space density of moderate-luminosity, obscured AGN
C. L. Barlow-Hall, J. Aird
https://arxiv.org/abs/2506.16145

Measurements of the z=4-10 X-ray Luminosity Function: the high space density of moderate-luminosity, obscured AGN
SMBHs are theorised to undergo significant growth in the early Universe, however, the X-ray Luminosity Function (XLF), used as a principal tracer of the SMBH accretion density, lacks observational constraints at z>6, until now. We present new measurements of the z=4-10 XLF at intermediate luminosities, taking advantage of recent deep near-IR imaging from UltraVISTA that enables us to identify galaxies and AGN at high redshifts within which we identify X-ray sources using Chandra COSMOS data. We…

Discrete VHCs for Propeller Motion of a Devil-Stick using purely Impulsive Inputs
Aakash Khandelwal, Ranjan Mukherjee
https://arxiv.org/abs/2508.15040 https://

Discrete VHCs for Propeller Motion of a Devil-Stick using purely Impulsive Inputs
The control problem of realizing propeller motion of a devil-stick in the vertical plane using impulsive forces applied normal to the stick is considered. This problem is an example of underactuated robotic juggling and has not been considered in the literature before. Inspired by virtual holonomic constraints, the concept of discrete virtual holonomic constraints (DVHC) is introduced for the first time to solve this orbital stabilization problem. At the discrete instants when impulsive inputs …

An unconditional lower bound for the active-set method in convex quadratic maximization
Eleon Bach, Yann Disser, Sophie Huiberts, Nils Mosis
https://arxiv.org/abs/2507.16648

An unconditional lower bound for the active-set method in convex quadratic maximization
We prove that the active-set method needs an exponential number of iterations in the worst-case to maximize a convex quadratic function subject to linear constraints, regardless of the pivot rule used. This substantially improves over the best previously known lower bound [IPCO 2025], which needs objective functions of polynomial degrees $ω(\log d)$ in dimension $d$, to a bound using a convex polynomial of degree 2. In particular, our result firmly resolves the open question [IPCO 2025] of whe…

Baseline behaviour in human vision
Thomas Fabian
https://arxiv.org/abs/2507.14573 https://arxiv.org/pdf/2507.14573

Baseline behaviour in human vision
Humans perceive their visual environment by directing their eyes towards relevant objects. The deployment of visual attention depends substantially on the stimulus's properties, higher cognitive processes, and biases and constraints of the visual system. Numerous models describe people's eye movements depending on the performed task or the viewed content. However, there is no universal, context-invariant model of human gaze behaviour. Here we show that statistical regularities can be utilised t…

Identifying Solution Constraints for ODE Systems
Nicolae Tarfulea
https://arxiv.org/abs/2507.15805 https://arxiv.org/pdf/2507.15805…

Identifying Solution Constraints for ODE Systems
This work develops a framework to discover relations between the components of the solution to a given initial-value problem for a first-order system of ordinary differential equations. This is done by using sparse identification techniques on the data represented by the numerical solution of the initial-value problem at hand. The only assumption is that there are only a few terms that connects the components, so that the mathematical relations to be discovered are sparse in the set of possible…

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Raj Jain, Marc Wetter
https://arxiv.org/abs/2508.15204 https://arxiv.org/pdf/2508.15204…

R-ConstraintBench: Evaluating LLMs on NP-Complete Scheduling
Effective scheduling under tight resource, timing, and operational constraints underpins large-scale planning across sectors such as capital projects, manufacturing, logistics, and IT fleet transitions. However, the reliability of large language models (LLMs) when reasoning under high-constraint regimes is insufficiently characterized. To address this gap, we present R-ConstraintBench, a scalable framework that evaluates models on Resource-Constrained Project Scheduling Problems (RCPSP), an NP-…

Observational Constraints on Scalar Field--Matter Interaction in Weyl Integrable Spacetime
Andronikos Paliathanasis
https://arxiv.org/abs/2506.16223 https:…

Observational Constraints on Scalar Field--Matter Interaction in Weyl Integrable Spacetime
We test an analytic cosmological solution within the framework of Weyl Integrable Spacetime using current observational data. In this model, dark energy is described by a pressureless fluid, while a scalar field arises naturally through the definition of the connection. This gravitational theory reveals a Chameleon Mechanism leading to a nonzero interaction between the scalar field and the matter sector. This model extends the standard $Λ$CDM cosmology by introducing one additional degree of f…

Observational constraints on phenomenological emergent dark energy and barotropic dark matter characterized by a constant equation of state parameter
Jian-Qi Liu, Yan-Hong Yao, Yan Su, Jia-Wei Wu
https://arxiv.org/abs/2507.16147

Observational constraints on phenomenological emergent dark energy and barotropic dark matter characterized by a constant equation of state parameter
While cold dark matter is widely supported by a range of cosmological observations, it encounters several difficulties at smaller scales. These issues have prompted the investigation of various alternative dark matter candidates, leaving the question "What is dark matter?" still open. In this work, we propose a new cosmological model that considers dark matter as a barotropic fluid with a constant equation of state parameter and interprets dark energy as the phenomenological emergent dark energ…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Ruihan Zhang, Jun Sun
https://arxiv.org/abs/2508.15642 https://arxiv…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Fairness in machine learning is more important than ever as ethical concerns continue to grow. Individual fairness demands that individuals differing only in sensitive attributes receive the same outcomes. However, commonly used machine learning algorithms often fail to achieve such fairness. To improve individual fairness, various training methods have been developed, such as incorporating fairness constraints as optimisation objectives. While these methods have demonstrated empirical effectiv…

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution
Jenny T. Wan (Kavli Institute for Particle Astrophysics & Cosmology, Stanford University), Philip Mansfield (Kavli Institute for Particle Astrophysics & Cosmology), Katherine A. Suess (University of Colorado, Boulder), Yunchong Wang (Kavli Institute for Particle Astrophysics & Cosmology, SLAC National Accelerator Laboratory), Sihan Yuan (Kavli Institute for Particle Astrophys…

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution
The advent of JWST has revolutionized the study of faint satellite galaxies at $z \gtrsim 1$, enabling statistical constraints on galaxy evolution and the galaxy$-$halo connection in a previously unexplored mass and redshift regime. We compare satellite abundances at $1 < z < 3.5$ from recent JWST observations with predictions from cosmological dark matter-only zoom-in simulations. We identify and quantify several sources of biases that can impact theoretical satellite counts, finding that assu…

Sub-GeV Sterile Neutrino as a Probe of Neutrino Mass Generation in the Minimal Left-Right Symmetric Model
Gang Li, Ying-Ying Li, Sida Lu, Ye-Ling Zhou
https://arxiv.org/abs/2508.15609

Sub-GeV Sterile Neutrino as a Probe of Neutrino Mass Generation in the Minimal Left-Right Symmetric Model
The minimal left-right symmetric model (mLRSM) provides an elegant and testable framework for addressing the origin of neutrino masses. We examine the constraints on the sub-GeV sterile neutrino in the type-II seesaw scenario of the mLRSM without left-right mixing, taking limits from collider searches, meson decays, supernovae, neutrinoless double beta ($0νββ$) decay and cosmology. Specifically, we derive the $0νββ$ decay constraints using the advanced effective field theory approach and …

Retrieval-Augmented Review Generation for Poisoning Recommender Systems
Shiyi Yang, Xinshu Li, Guanglin Zhou, Chen Wang, Xiwei Xu, Liming Zhu, Lina Yao
https://arxiv.org/abs/2508.15252

Retrieval-Augmented Review Generation for Poisoning Recommender Systems
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks, where malicious actors inject fake user profiles, including a group of well-designed fake ratings, to manipulate recommendations. Due to security and privacy constraints in practice, attackers typically possess limited knowledge of the victim system and thus need to craft profiles that have transferability across black-box RSs. To maximize the attack impact, the profiles often remains imper…

Koopman Operator Based Linear Model Predictive Control for 2D Quadruped Trotting, Bounding, and Gait Transition
Chun-Ming Yang, Pranav A. Bhounsule
https://arxiv.org/abs/2507.14605

Koopman Operator Based Linear Model Predictive Control for 2D Quadruped Trotting, Bounding, and Gait Transition
Online optimal control of quadrupedal robots would enable them to plan their movement in novel scenarios. Linear Model Predictive Control (LMPC) has emerged as a practical approach for real-time control. In LMPC, an optimization problem with a quadratic cost and linear constraints is formulated over a finite horizon and solved on the fly. However, LMPC relies on linearizing the equations of motion (EOM), which may lead to poor solution quality. In this paper, we use Koopman operator theory and …

Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design
Shumin Wang, Hajar El Hassani, Marco Di Renzo, Marios Poulakis
https://arxiv.org/abs/2507.15367

Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design
Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the joint design of the transmit precoding matrices and the RIS phase shift vector in a multi-user RIS-aided multiple-input multiple-output (MIMO) communication system. We formulate a max-min optimization problem to maximize the minimum achievable rate while considering transmit power and reradiation mask const…

Entanglement Entropy of Quantum Corners
Luca Ciambelli, Jerzy Kowalski-Glikman, Ludovic Varrin
https://arxiv.org/abs/2507.16800 https://

Entanglement Entropy of Quantum Corners
In gravitational theories with boundaries, diffeomorphisms can become physical and acquire a non-vanishing Noether charge. Using the covariant phase space formalism, on shell of the gravitational constraints, the latter localizes on codimension-$2$ surfaces, the corners. The corner proposal asserts that these charges, and their algebras, must be important ingredients of any quantum gravity theory. In this manuscript, we continue the study of quantum corner symmetries and algebras by computing t…

Chiral Invariant Mass Constraints from HESS J1731 347 in an Extended Parity Doublet Model with Isovector Scalar Meson
Yuk-Kei Kong, Bikai Gao, Masayasu Harada
https://arxiv.org/abs/2506.16684

Chiral Invariant Mass Constraints from HESS J1731 347 in an Extended Parity Doublet Model with Isovector Scalar Meson
The recent discovery of a central compact object (CCO) within the supernova remnant HESS J1731-347, with mass $0.77^{+0.20}_{-0.17} \ M_\odot $ and radius $10.4^{+0.86}_{-0.78}$ km is the lightest and smallest compact object ever observed. We identify it as an ultra-light Neutron star (NS) and constrain the chiral invariant mass of nucleon $m_0$ from the observational data of NS using an extended parity doublet model with including the isovector scalar meson $a_0(980)$. We study the higher orde…

Universal Dancing by Luminous Robots under Sequential Schedulers
Caterina Feletti, Paola Flocchini, Debasish Pattanayak, Giuseppe Prencipe, Nicola Santoro
https://arxiv.org/abs/2508.15484

Universal Dancing by Luminous Robots under Sequential Schedulers
The Dancing problem requires a swarm of $n$ autonomous mobile robots to form a sequence of patterns, aka perform a choreography. Existing work has proven that some crucial restrictions on choreographies and initial configurations (e.g., on repetitions of patterns, periodicity, symmetries, contractions/expansions) must hold so that the Dancing problem can be solved under certain robot models. Here, we prove that these necessary constraints can be dropped by considering the LUMI model (i.e., wher…

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello, Christophe Gueret, Masoud Mansoury, Michael Cochez, Martijn Schut
https://arxiv.org/abs/2508.13663

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve constraints that are inherently vague or context-dependent, such as preferences for attributes or related categories. Addressing this gap, we …

Euclid preparation: Expected constraints on initial conditions
Collaboration, Finelli, Akrami, Andrews, Ballardini, Casas, Karagiannis, Sakr, Valiviita, Alestas, Bartolo, Bermejo-Climent, Nesseris, Paoletti, Sapone, Tutusaus, Ach\'ucarro, Ca\~nas-Herrera, Jasche, Lavaux, Aghanim, Altieri, Amara, Amendola, Andreon, Auricchio, Baccigalupi, Bagot, Baldi, Bardelli, Battaglia, Biviano, Branchini, Brescia, Camera, Capobianco, Carbone, Carretero, Castellano, Castignani, Cavuoti, Chambers,…

Euclid preparation: Expected constraints on initial conditions
The Euclid mission of the European Space Agency will deliver galaxy and cosmic shear surveys, which will be used to constrain initial conditions and statistics of primordial fluctuations. We present highlights for the Euclid scientific capability to test initial conditions beyond LCDM with the main probes, i.e. 3D galaxy clustering from the spectroscopic survey, the tomographic approach to 3x2pt statistics from photometric galaxy survey, and their combination. We provide Fisher forecasts from t…

Wave solutions for hyperbolic systems
N. Manganaro, A. Rizzo
https://arxiv.org/abs/2507.16483 https://arxiv.org/pdf/2507.16483…

Wave solutions for hyperbolic systems
In this paper we propose a reduction procedure for determining generalized travelling waves for first order quasilinear hyperbolic nonhomogeneous systems. The basic idea is to look for solutions of the governing model which satisfy a further set of differential constraints. Some applications are given for a barotropic fluid with a source term.

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bazargul Matkerim, Changyou Li, Yali Zong, Bo Qi, Jingwei Kou
https://arxiv.org/abs/2507.16321

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited generalization capabilities. In this paper, a new solving scheme is proposed where the solution is iteratively updated following the updating of the physics-driven neural network (PDNN), the hyperparameters of which are optimized by minimizing the loss function which incorporates the constraints from the collected scattered…

On Prior Distributions for Orthogonal Function Sequences
Shonosuke Sugasawa, Daichi Mochihashi
https://arxiv.org/abs/2508.15552 https://arxiv.org/pdf/2508.…

On Prior Distributions for Orthogonal Function Sequences
We propose a novel class of prior distributions for sequences of orthogonal functions, which are frequently required in various statistical models such as functional principal component analysis (FPCA). Our approach constructs priors sequentially by imposing adaptive orthogonality constraints through a hierarchical formulation of conditionally normal distributions. The orthogonality is controlled via hyperparameters, allowing for flexible trade-offs between exactness and smoothness, which can b…

On Designing Modulation for Over-the-Air Computation -- Part I: Noise-Aware Design
Saeed Razavikia, Carlo Fischione
https://arxiv.org/abs/2506.15950 https:…

On Designing Modulation for Over-the-Air Computation -- Part I: Noise-Aware Design
Over-the-air computation (OAC) leverages the physical superposition property of wireless multiple access channels (MACs) to compute functions while communication occurs, enabling scalable and low-latency processing in distributed networks. While analog OAC methods suffer from noise sensitivity and hardware constraints, existing digital approaches are often limited in design complexity, which may hinder scalability and fail to exploit spectral efficiency fully. This two-part paper revisits and e…

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak
https://arxiv.org/abs/2508.15695

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
We present several advances to the physics and equality constrained artificial neural networks (PECANN) framework that substantially improve its capability to learn solutions of canonical partial differential equations (PDEs). First, we generalize the augmented Lagrangian method (ALM) to support multiple independent penalty parameters, enabling simultaneous enforcement of heterogeneous constraints. Second, we reformulate pointwise constraint enforcement and Lagrange multipliers as expectations …

Missing Physics Discovery through Fully Differentiable Finite Element-Based Machine Learning
Ado Farsi, Nacime Bouziani, David A Ham
https://arxiv.org/abs/2507.15787 https://

Missing Physics Discovery through Fully Differentiable Finite Element-Based Machine Learning
Although many problems in science and engineering are modelled by well-established PDEs, they often involve unknown or incomplete relationships, such as material constitutive laws or thermal response, that limit accuracy and generality. Existing surrogate-modelling approaches directly approximate PDE solutions but remain tied to a specific geometry, boundary conditions, and set of physical constraints. To address these limitations, we introduce a fully differentiable finite element-based machin…

A prolific repeating fast radio burst source and a crisis of the magnetar model
Jun-Shuo Zhang, Tian-Cong Wang, Pei Wang, He Gao, Qin Wu, Di Li, Weiwei Zhu, Bing Zhang, Ke-Jia Lee, Jinlin Han, Chao-Wei Tsai, Fayin Wang, Yong-Feng Huang, Yuan-Chuan Zou, Dengke Zhou, Wanjin Lu, Jintao Xie, Jianhua Fang, Jinhuang Cao, Chen-Chen Miao, Yuhao Zhu, Yunchuan Chen, Xiaofeng Cheng, Yinan Ke, Yong-Kun Zhang, Long-Xuan Zhang, Shuo Cao, Shiyan Tian, Zi-Wei Wu, Chunfeng Zhang, Jiarui Niu, Dejiang Zh…

A prolific repeating fast radio burst source and a crisis of the magnetar model
Magnetars are widely considered to be the central engine of fast radio bursts (FRBs), capable of driving coherent radio emission through magnetic energy release. The accumulation of burst energy can be employed as a probe to reveal the nature of FRB energy engine. Current observations of repeaters have not yet provided sufficient constraints on the magnetic energy budget for magnetar models. Here we report 11,553 bursts detected from the hyper-active repeating source FRB 20240114A over 214 days…

A Novel Approach to Differential Privacy with Alpha Divergence
Yifeng Liu, Zehua Wang
https://arxiv.org/abs/2506.17012 https://arxiv.…

A Novel Approach to Differential Privacy with Alpha Divergence
As data-driven technologies advance swiftly, maintaining strong privacy measures becomes progressively difficult. Conventional $(ε, δ)$-differential privacy, while prevalent, exhibits limited adaptability for many applications. To mitigate these constraints, we present alpha differential privacy (ADP), an innovative privacy framework grounded in alpha divergence, which provides a more flexible assessment of privacy consumption. This study delineates the theoretical underpinnings of ADP and co…

Interaction of polytropic dark energy in cosmological model: Constraints from observational data
Elangbam Chingkheinganba Meetei, S. Surendra Singh
https://arxiv.org/abs/2508.15187

Interaction of polytropic dark energy in cosmological model: Constraints from observational data
We investigate an interacting polytropic dark energy (PDE) model characterised by the equation of state $p_{d} = αρ_{d}^{\,1+\frac{1}β}$, where the interaction between dark energy and pressureless matter is modelled via a linear coupling term $Q = 3ηHρ_{d}$. The background dynamics are formulated by deriving the Hubble parameter in the interacting scenario, and the model parameters are constrained through a Markov Chain Monte Carlo (MCMC) analysis using three joint observational data sets:…

BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
Zezhi Liu, Shizhen Wu, Hanqian Luo, Deyun Qin, Yongchun Fang
https://arxiv.org/abs/2507.14582

BT-TL-DMPs: A Novel Robot TAMP Framework Combining Behavior Tree, Temporal Logic and Dynamical Movement Primitives
In the field of Learning from Demonstration (LfD), enabling robots to generalize learned manipulation skills to novel scenarios for long-horizon tasks remains challenging. Specifically, it is still difficult for robots to adapt the learned skills to new environments with different task and motion requirements, especially in long-horizon, multi-stage scenarios with intricate constraints. This paper proposes a novel hierarchical framework, called BT-TL-DMPs, that integrates Behavior Tree (BT), Te…

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Jeroen Spaans, Jesse Heyninck
https://arxiv.org/abs/2507.16067

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowe…

Cosmological correlators in gravitationally-constrained de Sitter states
Tuneer Chakraborty, Ashik H, Suvrat Raju
https://arxiv.org/abs/2507.15926 https://…

Cosmological correlators in gravitationally-constrained de Sitter states
We study cosmological correlators in de Sitter quantum gravity in the limit where $G_N \to 0$. This limit is distinct from a nongravitational QFT because the gravitational constraints still force states and observables to be de Sitter invariant. We first examine a class of perturbative correlators that, in gauge-fixed form, are represented by the expectation value of a product of elementary fields on the late-time boundary. We formulate Feynman rules for our computations and enumerate some nece…

ACME: Adaptive Customization of Large Models via Distributed Systems
Ziming Dai, Chao Qiu, Fei Gao, Yunfeng Zhao, Xiaofei Wang
https://arxiv.org/abs/2507.14802

ACME: Adaptive Customization of Large Models via Distributed Systems
Pre-trained Transformer-based large models have revolutionized personal virtual assistants, but their deployment in cloud environments faces challenges related to data privacy and response latency. Deploying large models closer to the data and users has become a key research area to address these issues. However, applying these models directly often entails significant difficulties, such as model mismatching, resource constraints, and energy inefficiency. Automated design of customized models i…

AMICO galaxy clusters in KiDS-1000: cosmological constraints and mass calibration from counts and weak lensing
G. F. Lesci, F. Marulli, L. Moscardini, M. Maturi, M. Sereno, M. Radovich, M. Romanello, C. Giocoli, A. H. Wright, S. Bardelli, M. Bilicki, G. Castignani, H. Hildebrandt, A. Kannawadi, L. Ingoglia, S. Joudaki, E. Puddu
https://

AMICO galaxy clusters in KiDS-1000: cosmological constraints and mass calibration from counts and weak lensing
We present the joint modelling of weak-lensing and count measurements of the galaxy clusters detected with the AMICO code, in the fourth data release of the Kilo Degree Survey (KiDS-1000). The analysed sample comprises about 8000 clusters, covering an effective area of 839 deg$^{2}$ and extending up to a redshift of $z = 0.8$. Stacked cluster weak-lensing and count measurements have been derived in bins of redshift and intrinsic richness, $λ^*$. Based on self-organising maps, we reconstructed …

Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications
Ricus Husmann, Sven Weishaupt, Harald Aschemann
https://arxiv.org/abs/2508.14715

Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications
In this paper, we present an extension to the recursive Gaussian Process (RGP) regression that enables the satisfaction of inequality constraints and is well suited for a real-time execution in control applications. The soft inequality constraints are integrated by introducing an additional extended Kalman Filter (EKF) update step using pseudo-measurements. The sequential formulation of the algorithm and several developed heuristics ensure both the performance and a low computational effort of …

PAPR Analysis for MIMO FTN Signaling with Gaussian Symbols
Zichao Zhang, Melda Yuksel, Gokhan M. Guvensen, Halim Yanikomeroglu
https://arxiv.org/abs/2507.15116

PAPR Analysis for MIMO FTN Signaling with Gaussian Symbols
Faster-than-Nyquist signaling serves as a promising solution for improving spectral efficiency in future generations of communications. However, its nature of fast acceleration brings highly overlapped pulses that lead to worse peak-to-average power ratio (PAPR) performance. In this paper, we investigate the PAPR behavior of MIMO FTN using Gaussian symbols under optimal power allocation for two power constraints: fixed transmit power and fixed received signal-to-noise-ratio (SNR). Our findings …

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training
Pengfei Du
https://arxiv.org/abs/2507.14202 https://

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet they pose significant security risks that threaten their safe deployment in critical domains. Current security alignment methodologies predominantly rely on Process Reward Models (PRMs) to evaluate intermediate reasoning steps, introducing substantial computational overhead and scalability constraints. This paper presents a novel PRM-free security alignment framework that leverages automated …

Improving ASP-based ORS Schedules through Machine Learning Predictions
Pierangela Bruno, Carmine Dodaro, Giuseppe Galat\`a, Marco Maratea, Marco Mochi
https://arxiv.org/abs/2507.16454

Improving ASP-based ORS Schedules through Machine Learning Predictions
The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different department units. Recently, solutions to this problem based on Answer Set Programming (ASP) have been delivered. Such solutions are overall satisfying but, when applied to real data…

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Nicholas Mohammad, Nicola Bezzo
https://arxiv.org/abs/2507.14700

Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor …

INTEGRAL, eROSITA and Voyager Constraints on Light Bosonic Dark Matter: ALPs, Dark Photons, Scalars, $B-L$ and $L_{i}-L_{j}$ Vectors
Thong T. Q. Nguyen, Pedro De la Torre Luque, Isabelle John, Shyam Balaji, Pierluca Carenza, Tim Linden
https://arxiv.org/abs/2507.13432

INTEGRAL, eROSITA and Voyager Constraints on Light Bosonic Dark Matter: ALPs, Dark Photons, Scalars, $B-L$ and $L_{i}-L_{j}$ Vectors
The decay of light bosonic dark matter particles can produce a bright electron/positron ($e^+e^-$) flux that can be strongly constrained by local Voyager observations of the direct $e^+e^-$ flux, as well as 511 keV Line and X-ray continuum observations of $e^+e^-$ emission. We carefully analyze the $e^+e^-$ yield and resulting cosmic-ray and X-ray spectra from theoretically well-motivated light dark matter models, including: (a) electrophilic axion-like particles, (b) dark photons, (c) scalars,…

Cosmological pole-skipping, shock waves and quantum chaotic dynamics of de Sitter horizons
Yongjun Ahn, Sa\v{s}o Grozdanov, Hyun-Sik Jeong, Juan F. Pedraza
https://arxiv.org/abs/2508.15589

Cosmological pole-skipping, shock waves and quantum chaotic dynamics of de Sitter horizons
We present a systematic analysis of pole-skipping for scalar, Maxwell, and gravitational waves in cosmological spacetimes. Specifically, working in empty de Sitter space and in Schwarzschild-de Sitter black hole geometries, we locate the tower of pole-skipping points of such fields and show that they impose nontrivial constraints on the corresponding bulk two-point functions. Focusing on the gravitational sound channel, we then extract the Lyapunov exponent and butterfly velocities that charact…

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Bo Hou, Xin Tan, Kai Zheng, Fang Liu, Yinghao Zhu, Li Zhang
https://arxiv.org/abs/2507.16395

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Atomic commits, each of which addresses a single development concern, are a best practice in software development. However, developers frequently produce tangled commits that mix unrelated changes due to practical constraints or unclear boundaries, negatively impacting code review and maintenance. Although prior commit untangling approaches: rule-based, feature-based, or graph-based, have made progress, they often rely on shallow signals and fail to distinguish between explicit dependencies (e.…

A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
Yanbo Chen, Yunzhe Tan, Yaojia Wang, Zhengzhe Xu, Junbo Tan, Xueqian Wang
https://arxiv.org/abs/2507.15607

A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
Autonomous navigation of vehicle-trailer systems is crucial in environments like airports, supermarkets, and concert venues, where various types of trailers are needed to navigate with different payloads and conditions. However, accurately modeling such systems remains challenging, especially for trailers with castor wheels. In this work, we propose a novel universal vehicle-trailer navigation system that integrates a hybrid nominal kinematic model--combining classical nonholonomic constraints …

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints
Roberto Morales, Umberto Biccari
https://arxiv.org/abs/2507.13983

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints
This article introduces a generalized framework for Decentralized Learning formulated as a Multi-Objective Optimization problem, in which both distributed agents and a central coordinator contribute independent, potentially conflicting objectives over a shared model parameter space. Unlike traditional approaches that merge local losses under a common goal, our formulation explicitly incorporates coordinator-side criteria, enabling more flexible and structured training dynamics. To navigate the …

A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
Junyi Jiang (Angela), Wei Chen (Angela), Xin Guo (Angela), Shenghui Song (Angela), Ying Jun (Angela), Zhang, Zhu Han, Merouane Debbah, Khaled B. Letaief
https://arxiv.org/abs/2506.16304

A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025. To understand the practical and fundamental values of 6G and facilitate its standardization, it is crucial to explore the theoretical limits of spectrum, energy, and coverage efficiency considering practical hardware and signaling constraints. In this paper, we present a mean-field-approximation-based investigation on two out of six use case scenari…

Density control of multi-agent swarms via bio-inspired leader-follower plasticity
Gian Carlo Maffettone, Alain Boldini, Mario di Bernardo, Maurizio Porfiri
https://arxiv.org/abs/2507.15781

Density control of multi-agent swarms via bio-inspired leader-follower plasticity
The design of control systems for the spatial self-organization of mobile agents is an open challenge across several engineering domains, including swarm robotics and synthetic biology. Here, we propose a bio-inspired leader-follower solution, which is aware of energy constraints of mobile agents and is apt to deal with large swarms. Akin to many natural systems, control objectives are formulated for the entire collective, and leaders and followers are allowed to plastically switch their role i…

Shadows from thin accretion disks of parametrized black hole solutions
Gonzalo J. Olmo, Jo\~ao Lu\'is Rosa, Diego Rubiera-Garcia, Alejandro Rueda, Diego S\'aez-Chill\'on G\'omez
https://arxiv.org/abs/2507.16580

Shadows from thin accretion disks of parametrized black hole solutions
We discuss the optical appearance from thin accretion disks in parametrized black holes, namely, solutions characterized by an arbitrarily large number of parameters without any regards to the theory of the gravitational and matter fields they come from. More precisely, we consider the leading-order terms of the spherically symmetric Johanssen-Psaltis (JP) and Konoplya-Zhidenko (KZ) parametrizations after imposing constraints from asymptotic flatness and solar system observations. Furthermore, …

Stringy Constraints on Modular Flavor Models
Keiya Ishiguro, Takafumi Kai, Tatsuo Kobayashi, Hajime Otsuka
https://arxiv.org/abs/2508.12392 https://arxiv.o…

Stringy Constraints on Modular Flavor Models
We investigate stringy constraints on moduli spaces in modular flavor models by analyzing moduli-dependent threshold corrections in heterotic string vacua. While moduli play a crucial role in determining the flavor structure of fermions predicted by modular flavor models, the parameter space in which their vacuum expectation values are allowed has not been fully explored. In this work, within the framework of perturbative heterotic string theory on toroidal orbifolds, we derive constraints on t…

WGRAMMAR: Leverage Prior Knowledge to Accelerate Structured Decoding
Ran Wang, Xiaoxuan Liu, Hao Ren, Gang Chen, Fanchao Qi, Maosong Sun
https://arxiv.org/abs/2507.16768

WGRAMMAR: Leverage Prior Knowledge to Accelerate Structured Decoding
Structured decoding enables large language models (LLMs) to generate outputs in formats required by downstream systems, such as HTML or JSON. However, existing methods suffer from efficiency bottlenecks due to grammar compilation, state tracking, and mask creation. We observe that many real-world tasks embed strong prior knowledge about output structure. Leveraging this, we propose a decomposition of constraints into static and dynamic components -- precompiling static structures offline and in…

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem
Kuangyu Ding, Kim-Chuan Toh
https://arxiv.org/abs/2507.15264

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem
We study a nonsmooth nonconvex optimization problem defined over nonconvex constraints, where the feasible set is given by the intersection of the closure of an open set and a smooth manifold. By endowing the open set with a Riemannian metric induced by a barrier function, we obtain a Riemannian subgradient flow formulated as a differential inclusion, which remains strictly within the interior of the feasible set. This continuous dynamical system unifies two classes of iterative optimization me…

Reanalyzing DESI DR1: 1. $\Lambda$CDM Constraints from the Power Spectrum and Bispectrum
Anton Chudaykin, Mikhail M. Ivanov, Oliver H. E. Philcox
https://arxiv.org/abs/2507.13433 …

Reanalyzing DESI DR1: 1. $Λ$CDM Constraints from the Power Spectrum and Bispectrum
We present the first independent re-analysis of the galaxy clustering data from DESI Data Release 1, utilizing an effective field theory (EFT)-based full-shape model. We analyze the power spectra and bispectra of the public catalogs using a custom-built pipeline based on window-deconvolved quasi-optimal estimators, carefully accounting for all relevant systematic effects. Compared to the official collaboration analysis, we add the galaxy power spectrum hexadecapole and the bispectrum monopole, …

CPL-parametrized cosmic expansion in Galileon gravity: Constraints from recent data
Amit Samaddar, S. Surendra Singh
https://arxiv.org/abs/2508.14392 https://

CPL-parametrized cosmic expansion in Galileon gravity: Constraints from recent data
We explore the cosmic expansion history within the framework of Galileon gravity by employing a redshift-based expression for the Hubble rate, $H(z)$, derived from the CPL parametrization $ω_{DE}(z)=ω_{0}+ω_{a}\frac{z}{1+z}$. This parametrization allows for a time-dependent expansion history consistent with the non-linear Galileon field equations. To constrain the model parameters, we perform a MCMC analysis using $46$ Hubble parameter measurements, DESI DR2 BAO data and $1701$ Pantheon+ dat…

Investigating $f(R)$-Inflation: background evolution and constraints
Elisa Fazzari, Chiara De Leo, Giovanni Montani, Matteo Martinelli, Alessandro Melchiorri, Guadalupe Ca\~nas-Herrera
https://arxiv.org/abs/2507.13890

Investigating $f(R)$-Inflation: background evolution and constraints
In this work, we investigate the possibility of generating an inflationary mechanism within the framework of a metric-$f(R)$ modified gravity theory, formulated in the Jordan frame. We explore whether the scalar field, non-minimally coupled to gravity and emerging in the Jordan frame, can play the role of the primordial inflaton. Particular attention is devoted to constructing a dynamical scenario in the Jordan frame that exhibits a slow-rolling phase for the scalar field and admits a quasi-de …

Comprehensively Constraining Ultra-Light Primordial Black Holes Through Relic Formation and Early Mergers
Amirah Aljazaeri, Christian T. Byrnes
https://arxiv.org/abs/2506.16154

Comprehensively Constraining Ultra-Light Primordial Black Holes Through Relic Formation and Early Mergers
We investigate constraints on the least explored, smallest mass scales of primordial black holes (PBHs), which evaporate prior to Big Bang Nucleosynthesis (BBN). Our study examines the impact of Planck-mass relics on the allowed fraction of dark matter composed of PBHs ($f_{PBH}$), as well as on the resulting stochastic gravitational wave background and the formation of primordial binaries. We discuss how these binaries and early mergers lead to longer PBH lifetimes, extending the reach of the …

Stabilizing simulation-based cosmological Fisher forecasts: a case study using the Voronoi volume function
Saee Dhawalikar (IUCAA), Aseem Paranjape (IUCAA), Shadab Alam (TIFR)
https://arxiv.org/abs/2506.16408

Stabilizing simulation-based cosmological Fisher forecasts: a case study using the Voronoi volume function
Forecasting cosmological constraints from halo-based statistics often suffers from instability in derivative estimates, especially when the number of simulations is limited. This instability reduces the reliability of Fisher forecasts and machine learning based approaches that use derivatives. We introduce a general framework that addresses this challenge by stabilizing the input statistic and then systematically identifying the optimal subset of summary statistics that maximizes cosmological i…

Going beyond $S_8$: fast inference of the matter power spectrum from weak-lensing surveys
Cyrille Doux, Tanvi Karwal
https://arxiv.org/abs/2506.16434 https…

Going beyond $S_8$: fast inference of the matter power spectrum from weak-lensing surveys
Weak lensing surveys are often summarized by constraints on the derived parameter ${S_8\equivσ_8\sqrt{Ω_{\rm m}/0.3}}$, obscuring the rich scale and redshift information encoded in the data, and limiting our ability to identify the origin of any tensions with $Λ$CDM predictions from the cosmic microwave background. In this work, we introduce a fast and flexible framework to extract the scale-dependent matter power spectrum $P(k, z)$ from cosmic shear and CMB lensing measurements, parameteriz…

Self-consistent population synthesis of AGN from observational constraints in the X-rays
D. Gerolymatou, S. Paltani, C. Ricci, M. Regamey
https://arxiv.org/abs/2506.14885

Self-consistent population synthesis of AGN from observational constraints in the X-rays
The cosmic X-ray background (CXB) is produced by the emission of unresolved active galactic nuclei (AGN), thus providing key information about the properties of the primary and reprocessed X-ray emission components of the AGN population. Equally important, studies of individual sources provide additional constraints on the properties of AGN, such as their luminosity and obscuration. Until now, these constraints have not been self-consistently addressed by intrinsically linking emission, absorpt…

Cosmological Preference for a Positive Neutrino Mass at 2.7$\sigma$: A Joint Analysis of DESI DR2, DESY5, and DESY1 Data
Guo-Hong Du, Tian-Nuo Li, Peng-Ju Wu, Jing-Fei Zhang, Xin Zhang
https://arxiv.org/abs/2507.16589

Cosmological Preference for a Positive Neutrino Mass at 2.7$σ$: A Joint Analysis of DESI DR2, DESY5, and DESY1 Data
Neutrinos and dark energy (DE) have entered a new era of investigation, as the latest DESI baryon acoustic oscillation measurements tighten the constraints on the neutrino mass and suggest that DE may be dynamical rather than a cosmological constant. In this work, we investigate the cosmological implications of simultaneously incorporating $\sum m_ν$ and $N_{\rm eff}$ within the framework of dynamical DE. A joint analysis of DESI DR2, cosmic microwave background, DESY5 supernova, and DESY1 wea…

Missing baryons recovered: a measurement of the gas fraction in galaxies and groups with the kinematic Sunyaev-Zel'dovich effect and CMB lensing
Boryana Hadzhiyska, Simone Ferraro, Gerrit S. Farren, Noah Sailer, Rongpu Zhou
https://arxiv.org/abs/2507.14136

Missing baryons recovered: a measurement of the gas fraction in galaxies and groups with the kinematic Sunyaev-Zel'dovich effect and CMB lensing
We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of Luminous Red Galaxies (LRGs) and Bright Galaxy Survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding $\log(M_{\rm ha…